Audioaudio-to-audio

Audio-to-Audio

Audio-to-audio encompasses speech enhancement, voice conversion, source separation, and style transfer — any task where audio goes in and transformed audio comes out. Speech enhancement (denoising) was revolutionized by Meta's Demucs and Microsoft's DCCRN, now used in every video call; voice conversion took a leap with RVC and So-VITS-SVC enabling zero-shot voice cloning that sparked both creative tools and deepfake concerns. Source separation (isolating vocals, drums, bass from a mix) reached near-production quality with HTDemucs and Band-Split RNN, making stems extraction a solved problem for most music. The field is converging toward unified models that handle multiple audio transformations through natural language instructions, blurring the line with text-to-audio generation.

2
Datasets
0
Results
si-snr
Canonical metric
Canonical Benchmark

DNS Challenge

Deep noise suppression on Microsoft DNS challenge data

Primary metric: si-snr
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Leading models on DNS Challenge.

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All datasets

2 datasets tracked for this task.

Related tasks

Other tasks in Audio.

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